Air　traffic　flow　management　is　one　of　the　most　important　operations　in　terminal　airports　heavily　relying　on　advanced　intelligence　transportation　techniques.　This　work　considers　a　two-stage　runway　scheduling　problem　given　a　set　of　flights　with　uncertain　arrival　times.　The　first-stage　problem　is　to　identify　a　sequence　of　aircraft　weight　classes　(e.g.,　Heavy,　Large　and　Small)　that　minimizes　runway　occupying　time　(i.e.,　makespan).　Then　the　second-stage　decision　is　dedicated　to　scheduling　the　flights　as　punctually　as　possible　after　their　arrival　times　realized,　which　translates　into　determining　a　sequence　of　flights　for　each　aircraft　category　such　that　the　total　deviation　time　imposed　on　the　flights　is　minimized.　Instead　of　an　exactly　known　probability　distribution,　information　on　uncertain　parameters　is　limited　(i.e.,　ambiguous),　such　as　means,　mean　absolute　deviations　and　support　set　of　random　parameters　derived　from　historical　data.　Under　this　information　on　the　random　parameters,　an　ambiguous　mixed-integer　stochastic　optimization　model　is　proposed.　For　such　a　problem,　we　approximately　construct　a　worst-case　discrete　probability　distribution　with　three　possible　realizations　per　random　parameter,　and　adopt　a　hybrid　sample　average　approximation　algorithm　in　which　genetic　algorithms　are　used　to　replace　commercial　solvers.　To　illustrate　the　effectiveness　and　efficiency　of　the　proposed　model　and　algorithm,　extensive　numerical　experiments　are　carried　out.